def start(self):
while True:
print(
'Commands: "show": display all category, "linear <category name>": display linear graph, "exit": exit programme, "coef": Show trening again product stats'
)
the_input = raw_input('Enter input:')
if the_input == 'exit':
break
elif the_input == 'show':
the_input = raw_input(
'Enter 1 for products, 2 for categories:')
hb = Myhbase('crawler')
if the_input == '1':
print(hb.getAllproducts())
elif the_input == '2':
print(hb.getAllcategories())
elif re.compile(r'^linear\s.+$').match(the_input):
category = re.compile(r'^linear\s(.+)$').match(the_input)
if category is not None:
self.lineargraph(category.group(1))
elif the_input == 'coef':
self.coef()
def start(self):
while True:
print('Commands: "show": display all category, "linear <category name>": display linear graph, "exit": exit programme, "coef": Show trening again product stats')
the_input = raw_input('Enter input:')
if the_input == 'exit':
break
elif the_input == 'show':
the_input = raw_input('Enter 1 for products, 2 for categories:')
hb = Myhbase('crawler')
if the_input == '1':
print(hb.getAllproducts())
elif the_input == '2':
print(hb.getAllcategories())
elif re.compile(r'^linear\s.+$').match(the_input):
category = re.compile(r'^linear\s(.+)$').match(the_input)
if category is not None:
self.lineargraph(category.group(1))
elif the_input == 'coef':
self.coef()
def coef(self):
hb_crawler = Myhbase('crawler')
hb_trend = Myhbase('trend')
data_hash = collections.defaultdict(dict)
categories = hb_crawler.getAllcategories()
for category in categories:
print(category)
#find coffient
X = []
Y = []
i = 1
rowkey = category + 'interests'
print(rowkey)
for key, data in hb_trend.table.scan(row_prefix=rowkey, ):
X.append([i])
v = int(data['stats:value'])
Y.append([v])
i += 1
if X:
mdl = LinearRegression().fit(X, Y)
m = mdl.coef_[0]
data_hash[category]['coef'] = m
min_price = None
max_price = None
mean_price = 0
total_price = 0
products_in_category = hb_crawler.getCategoryProducts(category)
for item in products_in_category:
if item['product:price']:
item_price = item['product:price']
regex = re.compile('[^0-9\.]')
item_price = regex.sub('', item_price)
item_price = float(item_price)
total_price += item_price
if item_price < min_price or min_price is None:
min_price = item_price
if item_price > max_price or max_price is None:
max_price = item_price
data_hash[category]['min_price'] = min_price
data_hash[category]['max_price'] = max_price
data_hash[category]['total_price'] = total_price
data_hash[category]['mean_price'] = total_price / len(
products_in_category)
data_hash[category][
'range_price'] = max_price - min_price
data_hash[category]['total_item'] = len(
products_in_category)
print(data_hash)
x = []
y = []
z = []
for key, item in data_hash.iteritems():
x.append(item['mean_price'])
y.append(item['coef'][0])
z.append(item['total_item'])
x = np.array(x)
y = np.array(y)
z = np.array(z)
colors = np.random.rand(len(x))
area = np.pi * z
plt.scatter(x, y, s=area, c=colors, alpha=0.5)
plt.title('Product range, price and trending coefficient', fontsize=20)
plt.xlabel('Averge Price', fontsize=15)
plt.ylabel('Trend Coefficient', fontsize=15)
plt.show()
def coef(self):
hb_crawler = Myhbase('crawler')
hb_trend = Myhbase('trend')
data_hash = collections.defaultdict(dict)
categories = hb_crawler.getAllcategories()
for category in categories:
print(category)
#find coffient
X = []
Y = []
i = 1
rowkey = category + 'interests'
print(rowkey)
for key, data in hb_trend.table.scan(row_prefix=rowkey, ):
X.append([i])
v = int(data['stats:value'])
Y.append([v])
i += 1
if X:
mdl = LinearRegression().fit(X, Y)
m = mdl.coef_[0]
data_hash[category]['coef'] = m
min_price = None
max_price = None
mean_price = 0
total_price = 0
products_in_category = hb_crawler.getCategoryProducts(category)
for item in products_in_category:
if item['product:price']:
item_price = item['product:price']
regex = re.compile('[^0-9\.]')
item_price = regex.sub('', item_price)
item_price = float(item_price)
total_price += item_price
if item_price < min_price or min_price is None:
min_price = item_price
if item_price > max_price or max_price is None:
max_price = item_price
data_hash[category]['min_price'] = min_price
data_hash[category]['max_price'] = max_price
data_hash[category]['total_price'] = total_price
data_hash[category]['mean_price'] = total_price / len(products_in_category)
data_hash[category]['range_price'] = max_price - min_price
data_hash[category]['total_item'] = len(products_in_category)
print(data_hash)
x = []
y = []
z = []
for key, item in data_hash.iteritems():
x.append(item['mean_price'])
y.append(item['coef'][0])
z.append(item['total_item'])
x = np.array(x)
y = np.array(y)
z = np.array(z)
colors = np.random.rand( len(x) )
area = np.pi * z
plt.scatter(x, y, s=area, c=colors, alpha=0.5)
plt.title('Product range, price and trending coefficient', fontsize=20)
plt.xlabel('Averge Price', fontsize=15)
plt.ylabel('Trend Coefficient', fontsize=15)
plt.show()